Long-term localization with map compression based on solar information.

In this paper we address visual based localization in outdoor environments where the appearance changes dra-matically. Such environmental changes result in a substantial transformation of the visual information of the scene, producing a significant impact on the visual based localization performance. Hence, these changes can lead to major difficulties when associating data between the current image and the landmarks in the map. One solution for this problem is to keep adding landmarks to the map in order to cover various environmental conditions. However, this solution leads to a continued growth of the map, which in turn, will result in a costly and resource-intensive localization. In this paper we present a map management approach in which we exploit information related to the suns position to compare resemblance between the traversals in the map and maintain a diverse map that incorporates a minimum amount of data and ensures a reliable localization in different environmental conditions. We evaluated our approach on a dataset that incorporates more than 100 sequences with different environmental conditions and we compared the obtained results with a state of the art approach.